Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming

Benjamin Alt, Urs Keßner, Aleksandar Taranovic, Darko Katic, Andreas Hermann +2 more
12/21/2023
cs.ROcs.AIcs.CL

No confident code implementation found for this paper. 8 possible matches can be shown below.

Abstract

Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 8 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Joint Verification and Refinement of Language Models for Safety-Constrained Planning

88% match2024

An LLM-powered Natural-to-Robotic Language Translation Framework with Correctness Guarantees

87% match2025

Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning

86% match2026

AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers

85% match1 repo752023

Large Language Models for Robotics: A Survey

85% match2023

Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction

85% match2024

Cite this paper

@article{alt2023domainspecific,
  title  = {Domain-Specific Fine-Tuning of Large Language Models for Interactive Robot Programming},
  author = {Benjamin Alt and Urs Keßner and Aleksandar Taranovic and Darko Katic and Andreas Hermann and Rainer Jäkel and Gerhard Neumann},
  year   = {2023},
  eprint = {2312.13905},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.13905v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2312.13905.svg)](https://codeofpaper.com/paper/2312.13905)

Discussion